Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes
(eg, gender, race, RNA sequencing technique) from dominating the clustering. Although a …

Fair clustering aims to hide sensitive attributes during data partition by balancing the
distribution of protected subgroups in each cluster. Existing work attempts to address this …

Deep clustering has the potential to learn a strong representation and hence better
clustering performance compared to traditional clustering methods such as $ k $-means and …

In this paper, we study the problem of fair clustering on the $ k-$ center objective. In fair
clustering, the input is $ N $ points, each belonging to at least one of $ l $ protected groups …

We propose a general variational framework of fair clustering, which integrates an original
Kullback-Leibler (KL) fairness term with a large class of clustering objectives, including …

In a recent work,\cite {chierichetti2017fair} studied the following``fair''variants of classical
clustering problems such as k-means and k-median: given a set of n data points in R^ d and …

We study the question of fair clustering under the {\em disparate impact} doctrine, where
each protected class must have approximately equal representation in every cluster. We …

We revisit the problem of fair clustering, first introduced by Chierichetti et al.(Fair clustering
through fairlets, 2017), which requires each protected attribute to have approximately equal …

We consider the problem of spectral clustering under group fairness constraints, where
samples from each sensitive group are approximately proportionally represented in each …

In recent years, the issue of fairness has become important in the field of machine learning.
In clustering problems, fairness is defined in terms of consistency in that the balance ratio of …